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What is End-to-End Machine Learning and why do I need it?

Glen Ferguson
Panelist Spotlight

Glen is a Data Scientist/Machine Learning Engineer, technology leader, and technical expert with proven career progression through various roles, He has extensive experience in data science, analytics, data engineering, machine learning, artificial intelligence, and scientific computational modeling. This experience spans various organizations, including consulting organizations, start-ups, enterprises, the U.S. Navy, and academic settings.

One of the most frustrating problems in data science is when one builds a model and has it sit on the shelf unused for years. To overcome this problem, machine learning needs to shift from building bespoke models that can solve issues to building machine learning systems. These systems can serve as a factory floor to build a multitude of models that can scale to production workloads. An apt term for this change is the development of end-to-end machine learning systems. These systems contain many elements that fall under MLOps but still include data science, data engineering, and other specializations. We will go over why this trend is needed, what parts make up a complete end-to-end machine learning system, and what are the benefits of the system.